基于免疫遗传算法的公交线网优化研究

Optimization of Transit Network Design Using Immune Genetic Algorithm

为了提高遗传算法在线网优化中的稳定性,在遗传算法过程中加入免疫因子的提取与注射,并设置局部最优的检测。改进后的免疫遗传算法能结合求解问题的特征信息对种群进行免疫接种,提高搜索速度和精度。通过路网验证,并与标准遗传算法进行比较,表明效果明显。

Although Genetic Algorithm（GA） has been applied to Transit Network Design Problem（TNDP）,slow evolutions and early convergences still remain unsolved.In order to improve the efficiency of GA,an improved algorithm based on the extraction and injection of vaccine is proposed in this paper.In addition,a detection mechanism is embedded in the algorithm to avoid local optimum.The Immune Genetic Algorithm（IGA） can improve the search speed and precision by vaccinating the population with the ＂special＂ characteristic information from the problem to be solved.The IGA is tested with a network,and the result is found to be much better when compared with standard GA.